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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Lunar Image Classification for Terrain Detection
verfasst von : Heng-Tze Cheng, Feng-Tso Sun, Senaka Buthpitiya, Ying Zhang, Ara V. Nefian
Erschienen in: Advances in Visual Computing
Verlag: Springer Berlin Heidelberg
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Terrain detection and classification are critical elements for NASA mission preparations and landing site selection. In this paper, we have investigated several image features and classifiers for lunar terrain classification. The proposed histogram of gradient orientation effectively discerns the characteristics of various terrain types. We further develop an open-source Lunar Image Labeling Toolkit to facilitate future research in planetary science. Experimental results show that the proposed system achieves 95% accuracy of classification evaluated on a dataset of 931 lunar image patches from NASA Apollo missions.